This paper presents a novel actuation model and control allocation strategy for a redundantly-actuated multirotor unmanned aerial vehicle (UAV), referred to as the omnicopter. With an unconventional configuration, the omnicopter's eight propellers are able to produce all the six components of net force/torque, with two degrees of actuation redundancy. This enables the vehicle to execute motion trajectories unattainable with conventional underactuated multi-rotors. A new inverse actuator model is proposed that accounts for the significant interactions between propeller airflows by relating their output thrust forces to their input motor commands. Actuation redundancy is resolved by solving a convex constrained optimization problem. Its solution yields the most power efficient set of propellers thrusts that would produce a required net force/torque, while respecting the propeller thrust limits. When the required force/torque is infeasible due to the thrust limits, the solution would minimize the norm of the error between the desired and actual net force/torque vectors. Experimental results demonstrate the effectiveness of the proposed model and control allocation strategy.

This paper proposes an on-board optimal controller that drives a train between two stations in an optimal time efficient, energy efficient, or mixed-objective manner, while adhering to a set of system-specific constraints. To this end, at each step along the track, the train control problem is formulated and solved as a constrained optimization problem over the remainder of the trip, while utilizing the latest train sensor data. The optimization problem is a convex second-order cone program. It incorporates knowledge of the track profile and relevant velocity and propulsion/braking constraints in the computation of the optimal propulsion/braking commands. It features an option to enforce a safety buffer between the train and another leading train on the track. The resulting convex optimization problem can be efficiently solved using a simple numerical solver, ensuring global optimality and robustness of the solution. Real-time performance and simulated closed-loop control results are presented, for a realistic vehicle and advanced trip model on desktop and embedded computer architectures.

A provably stable decentralized control scheme is proposed to allow multiple conventional quadcopters carry a cable-suspended payload. The method exploits a fundamental energetic passivity property of the combined drones, cables, and payload system to stably move the payload from its origin to destination. This is achieved without making any assumption about the status of the cables tension during the flight, and any measurement from the payload. The controller is decentralized in the sense that inter-drone communication of feedback measurements is not required. Motion stability is demonstrated via a Lyapunov analysis. The proposed controller is successfully implemented on a three-drone payload transport system in an indoor environment, using measurement from an optical tracking systems and the drones on-board IMUs.

This paper is concerned with off-line optimal control of an energy storage device in a grid-connected microgrid with the objective of minimizing the user electricity cost. The electricity prices and the microgrid net electricity demand, i.e. the difference between load any renewable generation, are assumed to be subject to uncertainties with known probabilistic distributions. A stochastic chance constraint optimization problem is formulated and solved off-line to determine optimal charge/discharge scheduling of the storage device. The objective function of the optimization problem is the expected value of the electricity cost and its constraints are of both deterministic and probabilistic nature. Deterministic convex nonlinear optimization formulations are derived which are equivalent to the original stochastic optimization problem, for two cases of uncertainties with normal and uniform distributions. These equivalent problems can be effectively solved using standard optimization algorithms to obtain an exact solution. Simulation results demonstrate the effectiveness of the proposed energy storage control methodology.

This paper proposes a novel initial rotor position estimation algorithm for Interior Permanent Magnet Synchronous Machine (IPMSM) drives. First, the rotor position is determined based on the machine saliency using the flux equations in the stationary reference frame. Since the machine saliency performs two periods in one electrical cycle, there exists an ambiguity of 180° in the estimation result. The location of the magnetic north pole is detected using a generalized polarity detection method. This method injects voltage pulses and compares the current response with the expected response using the d-axis differential inductance profile. An accurate nonlinear machine model is introduced for analysis and simulation of sensorless control in IPMSM drives. The model uses the machine flux as dynamic equation and the flux current relationship as output function avoiding approximations due to saturation. The initial position detection procedure is validated with this model using experimental current-flux data.

This paper is concerned with the mechanical design of a mobile manipulating unmanned ground vehicle (MM-UGV) coupled with an existing commercial unmanned aerial vehicle (UAV) to create a novel hybrid aerial/ground mobile manipulator. A novel systematic optimization-based approach is presented for making important design choices, such as the selection of gearboxes and electric DC motors in the drive-train, manipulator link lengths, and UGV base length. The objective is to minimize the overall mass of the MM-UGV. Constraints related to workspace, dynamic tip-over stability, actuator torque/force limits in static and dynamic motions, executed in the air or on the ground, and battery properties are incorporated into the problem formulation. The system operating conditions in the form of the range of end-effector forces, operating surface grade, and various position, velocity and acceleration variables are provided by the designer. The resulting problem is a robust bilevel nonlinear optimization, in which some of the constraints are derived from maximization/minimization over the operational variables to ensure constraint satisfaction in all possible operation scenarios. The problem is solved using a genetic algorithm. Numerical simulations demonstrate that the proposed strategy produces a design that meets the user-specified requirements.

This paper deals with motion control of under-actuated aerial robotic manipulators composed of multi-rotor Unmanned Aerial Vehicles and multi-link serial robotic arms. Kinematics and dynamics analysis of the multi-body mechanical system are performed. Using the method of virtual decomposition, adaptive motion control laws are proposed based on rigid-body inverse dynamics with feedback compensation. Underactuation imposes second-order nonholonomic constraints on the system dynamics, which are taken into account in the control algorithm. System stability and convergence of the tracking errors are proven using a Lyapunov analysis. A simulation case study demonstrates the effectiveness of the proposed model-based controller compared to a conventional controller.

Hardware-based parallel computing is proposed for acceleration of finite-element (FE) analysis of linear elastic deformation models. An implementation of the Preconditioned Conjugate Gradient algorithm on N Field Programmable Gate Array (FPGA) devices solves the large linear system of equations arising from the FE discretization. The system employs a large number of customized fixed-point computing units with a high-throughput memory architecture. An implementation of this scalable architecture on four Altera EP3SE110 FPGA devices yields a peak performance of 604 Giga Operations per second. This enables haptic simulation of a 3-dimensional deformable object of 21000 elements at an update rate of 400Hz.

An energy management system is proposed for a grid-connected microgrid with on-site battery storage and renewable energy sources. The system controls power flow between the microgrid and grid in order to effectively utilize renewable energy and maximize economic benefits for the customer and the utility operator. The power flow is optimized by formulating and solving a mixed-integer-linear-program optimization over a rolling horizon window. Multiple objectives concerning economic benefits, cost of battery operation and grid power profile shaping are built into the optimization. So called battery incremental red-zone power rates are among unique battery management features of this formulation. Careful consideration is also given to reducing the computations so the controller can run on an embedded computer in real time. Simulation results highlight the effectiveness of various novel aspects of the proposed controller.

This paper presents a mixed teleoperation/autonomous control approach for navigation and obstacle avoidance in mobile robots. The proposed method builds on an earlier general control framework that systematically combines teleoperation and autonomous control subtasks. This paper considers a scenario in which the user teleoperates a mobile robot while being assisted by an autonomous subtask designed to help avoid collisions with obstacles in the robot task environment. The autonomous subtask control commands are generated by formulating and solving a constrained optimization problem over a rolling horizon window of time into the future. The effectiveness of the proposed model-predictive control obstacle avoidance scheme is demonstrated in teleoperation experiments with a mobile robot using sonar measurements for obstacle localization.

A semi-autonomous control strategy is proposed for teleoperation of a mobile base and/or a twin-arm robotic manipulator for use when conventional teleoperation is inadequate. The approach employs idempotent and generalized pseudoinverse matrices to augment operator(s) control with some level of assistance/autonomy. An application specific task-space weighting matrix is introduced to adjust the relative weight of autonomous control with respect to human-centered teleoperation control. The task-space weighting matrix allows for a smooth continuous transition from completely autonomous to shared control, and to completely human teleoperated control. Experiments with a twin-armed mobile slave robot demonstrate the feasibility and basic functionality of the controller.

This paper is concerned with asymmetrical teleoperation, where the master/slave subsystems have different degrees of mobility. In particular, dual-master trilateral control of a possibly kinematically redundant slave robot (KRSR) and single-master control of a kinematically deficient slave robot (KDSR) are considered. In the case of a KDSR, the motion of the master robot is restricted to the natural motion constraint of the slave robot. Trilateral teleoperation is achieved via two master devices, each controlling a dedicated frame that is assigned on the slave robot. A novel control framework is presented that accomplishes two objectives: 1) motion and force tracking of the master and slave robots within their nonconstrained task spaces and 2) constrained master robot(s) motion that reflects the slave natural constraint (KDSR) or the kinematic constraint on each of the slave taskspace control frames (trilateral teleoperation). The proposed adaptive controller utilizes projection and generalized pseudoinverse matrices to achieve the stated teleoperation objectives. Stability and transparency of the asymmetric teleoperation system are demonstrated analytically and experimentally.

High-fidelity simulations of haptic interaction with deformable objects is computationally challenging. In this paper, hardwarebased parallel computing is proposed for finite-element (FE) analysis of soft-object deformation models. A distributed implementation of the Preconditioned Conjugate Gradient (PCG) algorithms on N Field Programmable Gate Array (FPGA) devices can solve the large system of equations arising from FE models at high update rates required for stable haptic interaction. Massive parallelization of the computations is achieved by customizing the hardware architecture to the problem at hand and concurrently employing a large number of adaptive fixed-point computing units. An implementation of this scalable hardware accelerator on four Altera EP3SE110 FPGA devices is capable of performing 230.4 Giga Operations per second in Sparse Matrix by Vector (SpMxV) multiplication. This architecture has successfully enabled real-time simulation of haptic interaction with a 3-dimensional FE model of 6000 nodes at an update rate of 200 Hz. Both static and dynamic linear elastic models have been successfully simulated.

Kinematically redundant robotic manipulators (KRRM) can provide a great degree of flexibility for working in complex unstructured environments. Teleoperation control of KRRM requires a strategy to resolve the redundancy of the slave robot while achieving transparency in the task space. In this paper, a two-master control approach is proposed in which the first master transparently controls the redundant slave end-effector in the task space, denoted as the primary task. Meanwhile, a second master exploits the slave redundancy to perform a secondary task such as obstacle avoidance or internal position control. Kinematic redundancy is considered for the slave robot and the traditional autonomous null-space control approach is also accommodated. Teleoperation control is achieved in two steps. First, velocity-level redundancy resolution is attained through new joint-space Lyapunov-based adaptive motion/force controllers. Coordinating reference commands for the joint-space controllers are designed to give priority to the primary task and decoupling between the tasks is achieved without the use of a dynamically consistent pseudo-inverse. Experimental results with two identical planar two-degree-of-freedom master devices controlling a simulated four-degree-of-freedom redundant slave robot show the effectiveness of the approach.

Communication time delay has been a major barrier to achieving high performance while maintaining stability in bilateral teleoperation. Building upon the results of our recent work in, a provably stable adaptive controller is proposed for variable delay teleoperation. The controller utilizes a model of the system dynamics and the time delay within a predictive control framework to improve the response transparency. It can also adapt to uncertainties in the user and environment dynamics. The performance objectives are delay-free position tracking between the master and slave and the establishment of a virtual mass-damper tool impedance between the user and environment. Delay reduction is accomplished based on a state observer and estimates of the system parameters. Using the delay reduced dynamics, an adaptive output regulation problem is formulated and solved. A Lyapunov-based analysis of the performance and stability of the resulting system is presented. Simulation results with a single-axis teleoperation setup demonstrate the effectiveness of the proposed approach.

Motivated by applications involving soft-tissue manipulation such as robotic surgery, the transparency objectives in bilateral teleoperation are redefined to include monotonic nonlinear and linear-time-invariant filter mappings between the master/slave position and force signals. To demonstrate the utility of the new performance measures, a stiffness discrimination telemanipulation task of soft environments is considered. A nonlinear force mapping can enhance stiffness discrimination thresholds as shown through a set of psychophysics experiments. Lyapunov-based adaptive motion/force controllers are presented that can achieve the new transparency objectives in the presence of dynamic uncertainty in the master, slave, user, and environment and in the absence of time delay. Givena prioriknown bounds on unknown dynamic parameters, a framework for robust stability analysis is proposed that uses an off-axis circle criterion and the Nyquist envelope of interval plant systems. Nonlinear- and linear-filtered mappings are achieved in experiments with a two-axis teleoperation system.

The control of time-delay bilateral teleoperation systems involves a delicate tradeoff between the conflicting requirements of transparency and robust stability. The control design is complicated by latency in data communication between the master and slave sites, as well as uncertainties in the dynamics of operator, master, slave, and environment. This paper proposes a systematic design procedure for improving teleoperation fidelity while maintaining its stability in the presence of dynamic uncertainty and a constant time delay. In a two-step control approach, first local Lyapunov-based adaptive/nonlinear controllers are applied to linearize the system dynamics and eliminate dependency on the master and slave parameters. Teleoperation coordination, subject to parametric uncertainty in the user and environment dynamics, is then achieved by formulating an I/O time-delay$H_{\infty }$robust control synthesis that is solved via its decomposition to the so-calledadobeproblems. The transparency and robust stability properties of the proposed method is examined via numerical analysis. Furthermore, the results are successfully validated in experiments.

An adaptive nonlinear controller is proposed that can couple impedance-type haptic devices with admittance-type virtual environments. The controller, which takes into account the nonlinear dynamics of the haptic device and parametric uncertainty in the userpsilas arm dynamics, replaces the natural dynamics of the interface with that of a mass-type virtual tool. The transparency and stability of the proposed haptic control system is investigated using a Lyapunov analysis. Low-pass filtering of the hand force measurement in a discrete-time implementation of the controller is shown to significantly reduce the lower bound on achievable synthesized inertia. The theoretical results are supported by experiments in which the proposed adaptive controller exhibits a far superior performance by reducing the haptic device interfering dynamics and rendering highly rigid contacts, when compared to a conventional spring-damper coupler.

In this work, a new hardware-based parallel implementation of the iterative conjugate gradient (CG) algorithm for solving such systems of equations is proposed. Fixed point computations are employed to optimize hardware resource usage and to increase parallelism. The proposed implementation adaptively adjusts to variations in the dynamic range of data operands in order to enhance computation accuracy and avoid divergence due to overflow and quantization errors.

High-fidelity haptic interaction in multi-user environments over general Ethernet-based local area networks (LAN) and metropolitan area networks (MAN) can be challenging but has promising applications. Under typical network traffic conditions, the 1kHz real-time control rate suggested in the literature for stable haptic simulation is well above that achievable by conventional network protocols such as the UDP and TCP/IP. To overcome this limitation, a decentralized multi-rate control approach is proposed in which local force-feedback loops are executed at higher rates than data packet transmission between the user workstations. Mathematical models for stability and performance analysis of such multi-rate haptic control systems are presented. Analytical and experimental results demonstrate improved performance and stability for the distributed control architecture when compared with a centralized controller.

The performance index in bilateral teleoperation, transparency, is often defined as linear scaling between the master and slave positions, as well as the operator and environment forces. Motivated by applications involving soft tissue manipulation such as robotic surgery, the transparency objective is generalized to include monotonic nonlinear mappings between the master/slave position and force signals. Modified Lyapunov-based adaptive motion/force controllers are presented that can guarantee the convergence of position and force tracking errors in the presence of dynamic uncertainty. Given a priori known bounds on the unknown operator and environment mass-spring-damper parameters, the closed-loop stability is analyzed using an off-axis circle criterion and the Nyquist envelope of interval plant systems. This approach produces far less conservative stability margins than those achievable by the passivity analysis. Experimental results with a two-axis teleoperation setup are provided.

The performance index in teleoperation, transparency, is often defined as linear scaling of force and position between the master/operator and slave/environment. Motivated by applications involving soft tissue manipulation such as robotic surgery, the transparency objective is generalized to include monotonic nonlinear mappings between the master/slave position and force signals. To demonstrate the utility of such performance index, an enhanced sensitivity non-linear force mapping design is proposed that can improve stiffness discrimination in telemanipulation tasks. The mapping design is validated using adaptive psychophysics perception experiments. Lyapunov-based adaptive motion/force controllers are presented that can guarantee the convergence of position and force tracking errors in the presence of dynamic uncertainty. Given a priori known bounds on the unknown operator/environment parameters, the robust stability of the proposed teleoperation system is analyzed using an off-axis circle criterion and the Nyquist envelope of interval plant systems. Experimental results with a two-axis teleoperation setup are provided.

Robust stability constraints can largely limit the performance of bilateral teleoperation systems in the presence of communication time delay. In this paper, a stable decentralized model-based controller is proposed that can enhance the teleoperation transparency in the presence of constant delay. New state/observation transformations produce partially delay-free dynamics/measurement vectors. Using the transformed states/observastions, two local linear quadratic Gaussian (LQG) controllers are implemented at the master and slave stations. The closed-loop stability is analyzed employing the delay-dependent frequency sweeping test. Using the LQG controllers and a simple control switching strategy, a multi- model teleoperation controller is developed that can achieve delay-free position tracking and tool impedance shaping for free motion/soft contact, as well as position and force tracking for contact with rigid environments. Experimental results demonstrate the effectiveness of the proposed approach.

Control of time-delay bilateral teleoperation systems requires a delicate balance between the conflicting requirements of transparency and robust stability. This manuscript proposes a systematic design procedure for improving teleoperation fidelity while maintaining its stability in the presence of constant communication delay and dynamic uncertainty. In a two-step control approach, first local Lyapunov-based adaptive/nonlinear controllers linearize the master/slave dynamics. Teleoperation coordination is then achieved by formulating an input/output (I/O) time-delay Hinfinrobust control synthesis which is solved via decomposition to adobe problems. The effectiveness of the proposed approach is demonstrated via numerical analysis and experimental results.

In this paper the problem of active object recognition/pose estimation is investigated. The principle component analysis is used to produce an observation vector from images captured simultaneously by multiple cameras from different view angles of an object belonging to a set of a priori known objects. Models of occlusion and sensor noise have been incorporated into a probabilistic model of sensor/object to increase the robustness of the recognition process with respect to such uncertainties. A recursive Bayesian state estimation problem is formulated to identify the object and estimate its pose by fusing the information obtained from the cameras at multiple steps. In order to enhance the quality of the estimates and to reduce the number of images taken, the positions of the cameras are controlled based on a statistical performance criterion, the Cramer-Rao lower bound (CRLB). Comparative Monte Carlo experiments conducted with a two-camera system demonstrate that the features of the proposed method, i.e. information fusion from multiple sources, active optimal sensor planing, and occlusion modelling are all highly effective for object classification/pose estimation in the presence of structured noise

While a conventional bilateral teleoperation system involves only one pair of master/slave robots, cooperative telerobotic systems can consist of multiple pairs of robotic manipulators. Due to dynamic interaction among slave manipulators as well as communication latency, the control of such systems can be particularly challenging. This paper presents a multimodel discrete-time controller for teleoperation in cooperative environments subject to a known constant communication delay. Discrete-time state-space models that explicitly incorporate signal delays are developed for free motion/soft contact and rigid contact phases of teleoperation. Mode-based linear quadratic Gaussian (LQG) controllers are proposed that can deliver a stable transparent response for each phase of operation. Switching among these controllers occur according to the identified phase of contact. The robustness of the controllers with respect to parametric uncertainty is examined via the Nyquist analysis. Simulation results demonstrate the effectiveness of the proposed approach

Prior efforts in bilateral teleoperation under communication delay have mainly yielded control algorithms that sacrifice performance in order to guarantee robust stability. In contrast, this paper proposes a multimodel predictive controller that can enhance the teleoperation transparency in the presence of a known constant delay. Separate controllers are designed for free motion/soft contact and contact with rigid environments, with switching between these mode-based control laws occurring according to the identified contact mode. Performance objectives such as position tracking and tool impedance shaping for free motion/soft contact, as well as position and force tracking for contact with rigid environments, are incorporated into a multi-input/multi-output state-space representation of the system dynamics. New Artstein-type state and measurement transformations are proposed to generate delay-free dynamics suitable for output-feedback control, based on the original dynamics with delays in various input and output channels. The application of the continuous-time linear quadratic Gaussian control synthesis to the resulting mode-based delay-free dynamics yields control laws that guarantee closed-loop stability and enhanced performance in each phase of teleoperation. The robustness of the mode-based controllers with respect to parametric uncertainty is analyzed. Experimental results with a single-axis teleoperation setup demonstrate the effectiveness of the proposed approach

This paper proposes a new adaptive control scheme for bilateral teleoperation in unknown environments. Traditional fixed-gain teleoperation methods often sacrifice performance in order to remain stable in the presence of large variations in the environment dynamics. In contrast, the proposed approach adjusts itself to the changes in the environment to maintain its stability without compromising performance. It is assumed that the dynamics of the environment are governed by a model from a finite set of environment models at any given time with Markov chain switching between these models. The first-order generalized pseudo-Bayesian (GPB1) multi-model estimation technique is used to identify the effective model at each time step given the sensory observations. The control action is a weighted sum of mode-based control laws that are designed for each mode of operation. Numerical and experimental studies demonstrate the effectiveness of the proposed method for teleoperation in free motion and in contact with rigid environments.

Cooperative teleoperation involves multiple operators interacting in a remote environment through multiple master and slave manipulators. This paper proposes a multi-lateral control architecture for teleoperation in cooperative environments. The proposed framework allows for transmission of position and force information between all master and slave robots rather than merely between corresponding units. A μ-synthesis-based control design is introduced for the proposed architecture. This approach guarantees robust stability of cooperative teleoperation in the presence of dynamic interaction between slave robots as well as unknown passive operators and environment dynamics. It also enhances task coordination by optimizing relevant cooperative performance objectives. Experimental studies carried out with a two-master/two-slave single-axis system demonstrate the effectiveness of the proposed approach.

This paper is concerned with the problem of appearance-based active multi-sensor object recognition/pose estimation in the presence of structured noise. It is assumed that multiple cameras acquire images from an object belonging to a set of known objects. An algorithm is proposed for optimal sequential positioning of the cameras in order to estimate the class and pose of the object from sensory observations. The principle component analysis is used to produce the observation vector from the acquired images. Object occlusion and sensor noise have been explicitly incorporated into the recognition process using a probabilistic approach. A recursive Bayesian state estimation problem is formulated that employs the mutual information in order to determine the best next camera positions based on the available information. Experiments with a two-camera system demonstrate that the proposed method is highly effective in object recognition/pose estimation in the presence of occlusion.

Cooperative telerobotic systems consist of multiple pairs of master/slave robotic manipulators operating in a shared environment. This paper presents a multilateral adaptive nonlinear control architecture for cooperative teleoperation. The proposed framework allows for transmission of position and force information between all master and slave robots rather than merely between corresponding units. An adaptive nonlinear controller establishes kinematic correspondence among masters and slaves. The operators are presented with a virtual intervening tool in order to collaboratively interact with the environment. Models of operators, master and slave robots, tool, and environment are incorporated in the design. The stability of the system in the presence of parametric uncertainty in the dynamics is proven via Lyapunov analysis. Simulation and experimental studies demonstrate that the proposed approach is highly effective in all phases of a teleoperation task, i.e. in free motion, in contact with a flexible environment, and in contact with a rigid environment.

This paper presents a multilateral control architecture for teleoperation in multimaster/multislave environments. The proposed framework incorporates flow of position and force information between all master and slave robots, rather than merely between corresponding units. Within this architecture, cooperative performance measures are defined to enhance coordination among the operators and the robots for achieving the task objectives. A /spl mu/-synthesis-based methodology for cooperative teleoperation control is also introduced. This approach guarantees robust stability of cooperative teleoperation in the presence of dynamic interaction between slave robots, as well as unknown passive operators and environment dynamics. It also improves task coordination by optimizing relevant performance objectives. Experiments carried out with a two-master/two-slave single-axis system demonstrate the effectiveness of the proposed approach.